Business leaders and managers deal with risk in a variety of forms. One form of risk involves making decisions on less than complete information. Since real world decisions seldom involve complete information, business leaders and managers often make decisions with incomplete data and little or no knowledge about the risk that the missing data creates. In such cases, business personnel make intuitive or “gut level” assessments.
One area in which business leaders and managers make decisions based upon incomplete data involves computer generated metric displays. Computer generated metric displays depend on periodically updated input data to provide meaningful and valid indicators. Such input data is based on historical data. For example, a company providing computer services might use a graph of server traffic over time to determine when to add or decrease capacity. A manufacturing company may compare final yield of a product to determine the impact of a variable on a particular process at different time intervals. But not all historical data is available for input when needed.
Input data can be incomplete because data is missing or because a time lag occurs between when an event takes place, and when the known effects of the event are determined. Incomplete input data impacts the computer generated metric display and therefore, the ability of the business leader or manager to use the metric to analyze risk. In either case, a gap in the data of the computer generated metric affects the quality of the decision based upon the metric.
Capabilities exist for analyzing historical performance data and using the historical performance data to predict missing data points. Therefore, a need exists to integrate predictive capabilities directly into business metrics for the purpose of providing a metric with actual and predicted data.